Much of customer support at LinkedIn is done via some form of online communication such as online feedback forms or email between members and support agents. Topic-based sentiment analysis of member feedback is critical since a single piece of feedback may address several different topics with different sentiment expressed in each. This talk addresses the topic-based sentiment analysis of customer support feedback focusing on the following questions 1) how do we find the most relevant topics of a product in question 2) how do we ensure to attribute sentiment to these specific topics as opposed to the feedback as a whole 3) how do we leverage natural language processing tools such as key phrase extraction and synonym identification to make the obtained topic-sentiment information best suitable for human consumption. The model proposed here is extendable to mining sentiment in reviews or any other sentiment-bearing text.